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On UMAP's true loss function

On UMAP's true loss function

26 March 2021
Sebastian Damrich
Fred Hamprecht
ArXivPDFHTML

Papers citing "On UMAP's true loss function"

13 / 13 papers shown
Title
Node Embeddings via Neighbor Embeddings
Node Embeddings via Neighbor Embeddings
Jan Niklas Böhm
Marius Keute
Alica Guzmán
Sebastian Damrich
Andrew Draganov
D. Kobak
GNN
69
0
0
31 Mar 2025
The Shape of Attraction in UMAP: Exploring the Embedding Forces in Dimensionality Reduction
The Shape of Attraction in UMAP: Exploring the Embedding Forces in Dimensionality Reduction
Mohammad Tariqul Islam
Jason W. Fleischer
247
0
0
12 Mar 2025
FedNE: Surrogate-Assisted Federated Neighbor Embedding for
  Dimensionality Reduction
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction
Ziwei Li
Xiaoqi Wang
Hong-You Chen
Han-Wei Shen
Wei-Lun Chao
FedML
34
0
0
17 Sep 2024
Sailing in high-dimensional spaces: Low-dimensional embeddings through
  angle preservation
Sailing in high-dimensional spaces: Low-dimensional embeddings through angle preservation
Jonas Fischer
Rong Ma
51
0
0
14 Jun 2024
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning
Shay Deutsch
Lionel Yelibi
Alex Tong Lin
Arjun Ravi Kannan
56
1
0
04 Jun 2024
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE
Aditya Ravuri
Neil D. Lawrence
28
1
0
27 May 2024
Relating tSNE and UMAP to Classical Dimensionality Reduction
Relating tSNE and UMAP to Classical Dimensionality Reduction
Andrew Draganov
Simon Dohn
FAtt
32
4
0
20 Jun 2023
Learning Topology-Preserving Data Representations
Learning Topology-Preserving Data Representations
I. Trofimov
D. Cherniavskii
Eduard Tulchinskii
Nikita Balabin
Evgeny Burnaev
S. Barannikov
23
20
0
31 Jan 2023
Unsupervised visualization of image datasets using contrastive learning
Unsupervised visualization of image datasets using contrastive learning
Jan Boehm
Philipp Berens
D. Kobak
SSL
26
15
0
18 Oct 2022
ParaDime: A Framework for Parametric Dimensionality Reduction
ParaDime: A Framework for Parametric Dimensionality Reduction
A. Hinterreiter
Christina Humer
Bernhard Kainz
M. Streit
30
5
0
10 Oct 2022
Uniform Manifold Approximation and Projection (UMAP) and its Variants:
  Tutorial and Survey
Uniform Manifold Approximation and Projection (UMAP) and its Variants: Tutorial and Survey
Benyamin Ghojogh
A. Ghodsi
Fakhri Karray
Mark Crowley
29
22
0
25 Aug 2021
Understanding How Dimension Reduction Tools Work: An Empirical Approach
  to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
Yingfan Wang
Haiyang Huang
Cynthia Rudin
Yaron Shaposhnik
174
306
0
08 Dec 2020
Attraction-Repulsion Spectrum in Neighbor Embeddings
Attraction-Repulsion Spectrum in Neighbor Embeddings
Jan Niklas Böhm
Philipp Berens
D. Kobak
34
53
0
17 Jul 2020
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